952 results about "Industrial robot" patented technology

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

The invention discloses a welding robot track self-adaptive correction method based on multi-modal data fusion, and particularly relates to the technical field of industrial robots, which comprises the following steps: synchronously acquiring visual three-dimensional point cloud and arc current and voltage transient waveform data in a welding process in real time; an enhanced working trajectory feature vector fusing the visual trajectory features and the arc deviation compensation amount is constructed; respectively calculating real-time confidence coefficients of the visual sense and the arc sensing mode; inputting the confidence coefficient into a weight distribution strategy model, dynamically resolving vision and arc weight coefficients, and when the confidence coefficient of one sensor is low, lifting the weight of the other sensor to dominate correction; performing weighted fusion on the two pose adjustment amounts by using a weight coefficient to generate a six-degree-of-freedom comprehensive trajectory correction amount; and finally, the robot is driven to execute online adaptive trajectory correction. Through a confidence-driven dynamic fusion strategy, the limitation of a single sensor is overcome, and the welding robot trajectory tracking precision and the adaptive capacity are improved.

The embodiment of the invention provides an industrial robot autonomous assembly task planning method based on a multi-agent large model, and belongs to the technical field of automatic assembly. The method comprises the steps of obtaining assembly task data of an assembly object, wherein the assembly task data comprises a task instruction for assembling the assembly object and an assembly manual; analyzing the task instruction and the assembly manual to obtain task execution logic; performing real-time acquisition on an assembly environment when the assembly object is assembled to obtain a target image; multi-modal sensing is conducted on the target image, a sensing result is obtained, and the sensing result comprises the position of at least one assembly of the assembly object in the assembly environment; and converting the task execution logic and the perception result into a planning domain definition language format, and performing assembly task planning through the behavior tree to obtain a target assembly task. According to the embodiment of the invention, the assembly efficiency and precision of the industrial robot are improved.

The invention discloses a finite time tracking adaptive control method for a mechanical arm based on a neural network, and relates to the technical field of industrial robot control. The method comprises the following steps: constructing a kinetic model of the mechanical arm, obtaining an existence form of an unknown nonlinear term in the model, and defining a joint position tracking error and an error change rate of the mechanical arm; constructing a sliding mode dynamic equation based on the tracking error and the error change rate; a BP neural network is adopted to approach the unknown nonlinear dynamic state of the mechanical arm, and the mapping relation between a network input vector and an output vector is determined; combining a sliding mode dynamic equation with BP neural network output, and designing a finite time control method including adaptive gain; and a self-adaptive updating method of BP network weight and sliding mode gain is deduced, so that the tracking error of the mechanical arm is converged to a zero neighborhood within preset time, and self-adaptive control of the mechanical arm is completed. According to the method, high-precision trajectory tracking within the preset time can be realized, and the anti-interference capability is high.

The invention discloses an intelligent manufacturing system and method based on an industrial robot, and relates to the technical field of industrial robot control, and the method comprises the steps: constructing a workpiece posture-space structure topological graph according to a cross-scale visual recognition result, carrying out the space comparison with an original trajectory planning graph, outputting a space deviation mapping relation, and carrying out the calculation of a spatial deviation mapping relation; performing spatial correlation analysis and path reachability evaluation on the workpiece attitude-spatial structure topological graph by using a graph neural network to obtain an end execution path instruction; driving the industrial robot to perform intelligent manufacturing operation through the tail end execution path instruction, and obtaining an actual operation result image; and performing feature alignment and difference comparison processing on the actual operation result image and the process reference image to obtain an operation deviation feature mapping relation, performing operation quality evaluation, and outputting an operation result visual detection label. According to the method, the adaptability and the manufacturing precision of the operation path of the industrial robot are effectively improved.

The invention discloses an industrial robot intelligent obstacle avoidance control method and system based on visual identification, and relates to the technical field of robot obstacle avoidance control, and the method comprises the steps: carrying out the calibration of a multi-mode visual sensor, obtaining an initial depth map and point cloud information, and combining the visual identification and depth compensation technology; recognizing and positioning obstacles in the operation area, and constructing a dynamic environment map layer; and the current robot state is collected, kinematics calculation and collision distance analysis are carried out, whether obstacle avoidance operation needs to be executed or not is judged, if yes, an obstacle avoidance path is generated in combination with the dynamic environment map layer and the target point location, feasibility verification is carried out after the path is generated, and the path passing the verification serves as an execution track to be issued to the control module. According to the invention, the recognition precision and depth perception integrity of the industrial robot on obstacles in a complex environment are improved, high feasibility of path planning and high-reliability obstacle avoidance capability in a dynamic environment are realized, and the intelligent decision-making level and operation safety of the system are remarkably enhanced.

A method of programming an industrial robot (100), with a robot manipulator (110) and robot controller (120), comprises: recording movements of the robot manipulator during a demonstration-based robot programming session, for thereby obtaining a robot trajectory; acquiring a video of the robot programming session; and generating, on the basis of the robot trajectory and the video, a robot program (C) executable by the robot controller, wherein the robot program includes at least one motion command which is expressed in a first reference frame (0 1). The method further comprises capturing operator input data indicating a first object (151) in an image in the video. The generated robot program includes a command to identify a position of an object resembling the first object in a work area (150) of a robot which executes the robot program; and the first reference frame is defined with respect to the identified position of the object resembling the first object.

The invention relates to the technical field of industrial robot control, and discloses an industrial robot trajectory optimization control method based on an intelligent algorithm. The method comprises the steps that joint position information, tool center point coordinates and a motion time sequence when a robot executes multiple tasks are collected and stored in a track database; after cleaning and screening data, extracting a feature set containing a path point sequence, speed distribution and an acceleration contour; constructing an intelligent optimization algorithm model of a neural network structure, and training by using the feature set to learn a trajectory optimization strategy; analyzing a target position coordinate and a motion constraint condition of the current task to obtain an initial track parameter; and inputting the initial parameters into the trained model, outputting an optimized track sequence containing a path point list and a speed curve, and generating a control instruction to drive the robot to move. The method can adapt to different tasks, improves track rationality and motion stability, and fits industrial production practice.

The invention relates to the technical field of industrial mechanical arm control, in particular to a mechanical arm execution control method for industrial production, which comprises the following steps: collecting parameters in real time; generating a geometric risk index; generating a mechanical risk index; extracting image features; abnormal mechanical arm judgment; determining an adjusting mechanical arm; and generating an adjustment instruction. According to the method, the inclination angle, the plane offset distance, the contact image features, the tightening torque and the force value of the tail end of the mechanical arm are monitored in real time in a multi-dimensional mode, a dual-risk judgment model based on geometric offset and mechanical loading is constructed, image texture fluctuation and posture change are further combined, the continuously abnormal mechanical arm is accurately positioned, and the safety of the mechanical arm is improved. And geometric and mechanical risk indexes are evaluated again after adjustment, so that the aluminum shell positioning and bolt assembling precision is guaranteed, and the problems that the production efficiency is reduced and the product defect rate is increased due to assembling quality fluctuation caused by mechanical arm positioning errors and inaccurate force control are effectively solved.

The invention relates to an intelligent path planning technology, and discloses an industrial robot multi-target path planning method based on a quantum annealing algorithm, and the method comprises the steps: obtaining the environment information of an industrial site; path planning is carried out based on the environment information, and a quantum Hamiltonian is constructed according to an optimization target of a path planning problem; encoding each optional path in the path planning space by adopting quantum bits, and initializing the quantum bits to be in a uniform superposition state; quantum system energy is gradually reduced through a quantum annealing algorithm, and an optimal solution of quantum bits is searched; after quantum annealing is finished, a final path is extracted; carrying out smooth optimization processing on the extracted final path; and generating a motion instruction of the industrial robot according to the optimized final path. The invention further discloses a control system, the industrial robot and a computer readable storage medium. According to the method, the global optimality of path planning is improved while multi-target path planning is efficiently carried out on the industrial robot.

The invention belongs to the technical field of industrial robot inspection, and discloses an instrument identification method for industrial robot inspection, an instrument identification system comprises a pose adjustment module, a lightweight detection module, an image double enhancement processing module and a double-branch instrument reading identification module, and the instrument identification method comprises the following steps: obtaining an instrument image, inputting the instrument image into an image dual-enhancement processing module to obtain a preprocessed instrument image; the preprocessed instrument image is input into a lightweight detection module, an instrument target detection result is output, the instrument type is judged according to the result, and an instrument reading result is output and recorded. According to the method, the improved lightweight target detection model and the adaptive image optimization processing algorithm are combined, the real-time performance and the high efficiency of instrument reading in an industrial scene are effectively improved, more intelligent and accurate instrument reading and industrial inspection can be provided, and powerful support is provided for information acquisition in a complex industrial environment.

The invention provides an unmanned system security monitoring method based on multi-modal perception and autonomous decision discrimination, belongs to the technical field of artificial intelligence and industrial automation crossing, and aims to solve the problem that a machine learning decision security monitoring system of an existing unmanned system is difficult to quickly perceive environment change and make accurate response. The method solves the problems that in a high-dynamic scene, recognition of sudden risks is lagged, decision adjustment is slow, and the safety and stability of task execution are affected in the prior art, and comprises the steps that S1, key parameters of an unmanned autonomous system are screened; s2, modeling a parameter coupling relation; s3, time sequence driven key parameter extreme value boundary modeling and statistical analysis are carried out; s4, discovering danger cognition; s5, building a real-time prediction framework; s6, constructing a risk assessment model; and S7, determining an optimal safety switching mechanism. The method is suitable for real-time safety control of unmanned equipment such as an unmanned aerial vehicle, an automatic driving vehicle and an industrial robot.

The invention discloses an industrial robot multi-machine cooperative interaction method and system based on artificial intelligence. The method comprises the following steps: S1, collecting and preprocessing interaction data of an industrial robot; s2, constructing a cooperative relation graph formed by industrial robot nodes and interaction edges; s3, disassembling the target task by adopting a priority mechanism and generating an execution weight matrix; s4, in combination with the interaction data and the weight matrix, generating a cooperative action vector through a multi-target reinforcement learning algorithm; s5, action conflict detection is executed, path overlapping and resource conflicts are eliminated, and a candidate action sequence is obtained; s6, generating a path and a control instruction, and forming a distributed execution instruction stream; and S7, driving the robot to execute the task, collecting feedback, updating the atlas, and circularly executing until the task is completed. According to the invention, task cooperation, path obstacle avoidance and dynamic optimization control among multiple industrial robots are realized, and the scheduling efficiency, the execution stability and the system intelligence level are effectively improved.

The invention discloses a thermal shock failure prediction and laser repair device for a thermal barrier coating and an application method. The device comprises a multi-physics field coupling simulation analysis module, a thermal shock experiment loading module, an online damage monitoring and failure diagnosis module, a robot laser repair execution module and a central control and data processing core unit. A thermal-mechanical-chemical coupling model is established to predict a coating failure behavior, a high-frequency induction heating and gas quenching system is adopted to simulate an extreme thermal shock environment, a multi-band thermal infrared imager, an ultrasonic probe and a digital image related system are integrated to monitor damage evolution in real time, and in-situ repair of a damaged area is realized based on a six-degree-of-freedom industrial robot. According to the method, the whole-process closed-loop control from failure prediction to repair regeneration is realized, the problems of single function and lack of multi-field coupling analysis and real-time repair capability in the prior art are solved, and the service reliability evaluation precision and the remanufacturing efficiency of the thermal barrier coating are remarkably improved.

The invention discloses an industrial robot predictive maintenance system based on machine learning, and relates to the technical field of industrial robot maintenance. Comprising a data acquisition module, a preprocessing module, a feature engineering module, a hybrid prediction model unit, a meta-learning and domain adaptive module, a federated learning coordination module, a digital twin sample generation module and a dynamic decision engine unit. A model agnostic meta-learning algorithm is utilized to train a cross-brand universal feature extractor, distribution of different brand data is confused in combination with an adversarial field adaptive network, a'data gap 'caused by sensor parameter definition and sampling frequency difference in traditional modeling is broken through, model cooperative training is achieved on the premise that data of all brands are not out of the local, and the modeling efficiency is improved. A global model containing cross-brand fault generality is generated, and the problem of data islands caused by commercial secrecy requirements in an industrial scene is solved.

The invention discloses a body intelligent controller architecture based on an end side intelligent model and an operation method, and belongs to the technical field of industrial body intelligence. According to the architecture, a CPU, a GPU and an FPGA are integrated by adopting a heterogeneous multi-core processor, and high-speed interconnection is realized through a star-type + NoC hybrid topology; a multi-level task scheduling mechanism is constructed based on a real-time operating system, and it is ensured that control tasks are executed preferentially. End-side visual model lightweight (YOLOv5s model parameters are reduced to 3.5 M) and FPGA hardware acceleration (control instruction time delay is 10ns) are innovatively realized on an algorithm framework layer, and a transmission bottleneck is eliminated through a zero-copy data path. The system supports model hot switching less than or equal to 10ms and FPGA bit stream online updating less than or equal to 50ms, and has MD5 check and an automatic rollback mechanism. Compared with a traditional separation type scheme, the processing time delay is reduced by 25 times, the hardware cost is reduced, the size is reduced, and the real-time performance, reliability and deployment flexibility of the industrial robot and other intelligent agents with the body are remarkably improved.

The invention discloses an operation analysis method and system for a mobile robot fused with an industrial mechanical arm, and the method comprises the steps: receiving a work order, then splitting a task, determining sub-task parameters, and optimizing the predicted time based on historical data; mapping and positioning are synchronized in the moving process, and the positioning precision is guaranteed through an SLAM model and prediction-correction logic; planning a global path and a residence position, and coping with a dynamic obstacle through a weighted scoring method and an MPC model; the operation position is precisely aligned to compensate the ground unevenness error; recognizing a workpiece and selecting an optimal grabbing point; the contact force and rigidity are dynamically adjusted when operation is executed, and automatic retreating and fine adjustment are performed when abnormity occurs; the grabbing quality and the advancing safety are confirmed; and data is updated after ending, and low-power automatic charging is realized. According to the method, the problems that a traditional technology path and operation planning are separated, sensing is single, robustness is poor, an operation strategy is difficult to adapt, and a standardized quick change and calibration mechanism is lacked are solved.

The invention discloses a 3D printing path optimization method for a multi-joint industrial robot, and relates to the field of 3D printing path optimization. The method comprises the steps that a normalized torque load index is generated according to the joint torque of the mechanical arm, and a layer cutting thickness sequence is generated in combination with the model surface curvature; a vibration risk index is constructed through the joint angular acceleration, the attitude interpolation weight is dynamically adjusted, and an attitude interpolation trajectory is generated; according to the distribution characteristics of the temperature field around the path point, calculating a thermal coupling factor, generating a thermal avoidance path offset, and correcting the path point; calculating a collision risk score based on the minimum Euclidean distance between the path point and the obstacle model; and fusing the cutting layer thickness, the vibration risk, the thermal coupling factor and the collision risk score, and constructing a global path scoring function. Through comprehensive consideration of mechanical arm load, vibration risk, heat accumulation and collision risk, printing precision and quality are effectively improved, path smoothness is optimized, vibration and heat influences are reduced, material deformation is avoided, and stability of the printing process is ensured.

The invention discloses an industrial robot motion planning system and method based on artificial intelligence, and belongs to the field of industrial robot intelligent obstacle avoidance. Based on a visual sensor, a depth sensor and a material priori library, feature extraction and attribute judgment are performed on an obstacle, and the risk is preliminarily evaluated; based on a force control sensor at the tail end of the robot and low-speed contact motion, contact force and displacement curve changes are obtained, the deformable characteristic of an obstacle is evaluated, and based on a deformation potential value, contact force and displacement curve analysis, contact stress calculation and obstacle energy absorption calculation, the damage risk of the obstacle is evaluated. The contact impact energy and the damage coefficient of the robot are evaluated based on the quality, the running speed and the anti-seismic coefficient of the industrial robot, the danger of the robot facing the obstacle is comprehensively evaluated based on the obstacle initial judgment risk, the damage probability and the robot damage coefficient, and the working efficiency and safety of the industrial robot are improved.

The invention discloses an industrial robot motion state intelligent control method based on a twinborn model, and the method comprises the steps: collecting the motion state parameters, such as the joint angular velocity, the joint angular displacement, the position and posture of an end effector, of a robot through a multi-domain coupling twinborn modeling engine; transmitting to an industrial internet twinborn intelligent control center for classified storage, and establishing a parameter association mapping table; constructing a multi-domain coupling twinborn model based on the parameters and the mapping table, inputting motion state representation data output by the model into a deep enhanced evolutionary control algorithm with spatio-temporal attention enhancement, and generating multiple groups of control instruction candidate sets after spatio-temporal dimension features are extracted; and the center evaluates and screens the candidate set, selects an instruction meeting the requirement and transmits the instruction to a robot execution mechanism. The method depends on a space-time attention enhancement algorithm to improve the control precision so as to complete a control link to avoid a process fault, the intelligence and reliability of robot motion control are effectively improved, and the method is suitable for robot motion regulation and control under complex industrial working conditions.

The invention discloses a dragging positioning spraying track planning method for a local area of the surface of special equipment and a storage medium, and belongs to the technical field of industrial robot automation. The method comprises the steps of obtaining point set data of a to-be-sprayed area; calculating a convex hull based on the point set, and solving a minimum enclosing rectangle by using a rotary caliper method to determine an accurate rectangle spraying boundary; constructing a rectangular amplitude limiting sedimentary model through spraying testing and parameter identification; generating a grid track with a buffer area based on the model and the boundary, and optimizing the spraying speed by taking the minimization time as a target; and for the curved surface, performing B-spline curved surface reconstruction and extracting a normal vector to control the attitude of the spray gun. According to the method, the complex multi-parameter trajectory optimization problem is simplified into single-speed optimization through the rectangular amplitude limiting deposition model, the problems that in local spraying, trajectory planning is complex, edge control is not precise, a coating is not uniform and the like are effectively solved, and the spraying efficiency and quality are remarkably improved.

The invention discloses an industrial robot motor fault early warning method, and relates to the technical field of motor testing and state monitoring, and the method comprises the steps: introducing a kurtosis-based real-time statistical feature to adaptively adjust the noise amplitude and the decomposition frequency, enabling noise-added empirical mode decomposition to dynamically reconfigure parameters along with the working condition of a motor, and enabling the noise-added empirical mode decomposition to be more accurate. Stable intrinsic mode function components can still be obtained when the impact load frequently changes; a screening coefficient formed by three types of sub-indexes of correlation, an energy ratio and impact sensitivity is utilized to realize automatic elimination of noise dominant components, and the problems of modal aliasing and residual noise are effectively relieved; on this basis, target components are reconstructed and fractal box dimensions are extracted, so that the features still have strong distinguishing ability in the face of transient abrupt change and multi-scale roughness, and the sensitivity to early weak faults is enhanced; and vibration signal dynamic time warping with current as a time reference is further combined with multi-sensor feature fusion.

The invention discloses an in-service precision intelligent diagnosis and calibration industrial robot system and a diagnosis and calibration method. The system comprises a parameter acquisition module, a fault diagnosis engine module, a collaborative calibration algorithm module, a laser tracking calibration module, an execution calibration module and a result feedback module. According to the method, joint, tail end, load and environment parameters of the robot are collected through the parameter collection module and transmitted to the fault diagnosis engine module, and features are extracted and faults are classified through a specific technology; the collaborative calibration algorithm module calculates a calibration compensation amount in combination with the fault parameters, and the laser tracking calibration module tracks and measures the motion trail of the robot in real time; the execution calibration module adjusts controller parameters to complete calibration, and the result feedback module compares the parameters and adjusts the acquisition frequency and type. According to the system and the method, in-service real-time diagnosis and calibration of the robot are realized, the fault diagnosis accuracy and the calibration precision stability are improved, the production interruption is reduced, the flexible production requirement is met, and the operation precision and the production efficiency of the robot are guaranteed.

The invention relates to the technical field of industrial robot fault diagnosis, and discloses a fault early warning and remote diagnosis method for an industrial robot electrical control system, and the method comprises the steps: obtaining a mechanical load parameter through a potential load model, an inertia load model and a basic dynamics model, obtaining an electrical state parameter through an electrical efficiency model, and carrying out the fault early warning and remote diagnosis. A correction factor is generated through a correction model in combination with the environment temperature, a dynamic current threshold value is calculated through a threshold value model based on the correction factor and a basic current threshold value, and finally health scores and fault early warning information are output through a diagnosis model in combination with the vibration acceleration and servo ring following errors. According to the method, mechanical, electrical and environmental multi-source parameters are fused, dynamic threshold adjustment and multi-dimensional health assessment are realized, normal working conditions and abnormal states are effectively distinguished, the fault early warning accuracy is remarkably improved, the false alarm rate is reduced, and a reliable remote diagnosis scheme is provided for the industrial robot.

The invention discloses an industrial robot programming teaching method and system, and belongs to the technical field of programming teaching, and the method specifically comprises the steps: collecting programming behavior data and industrial robot operation data in real time, generating learning state data of students according to the programming behavior data and the industrial robot operation data, and storing the learning state data of the students; the learning state data of the student comprises a programming behavior, a knowledge mastering degree and a learning style of the student, determining a knowledge point node embedding offset in combination with a preset dynamic knowledge graph according to the learning state data of the student, and updating the current dynamic knowledge graph according to the knowledge point node embedding offset, determining a current knowledge difficulty of the student based on the current dynamic knowledge graph, and pushing difficulty analysis data to a learning terminal of the student based on the current knowledge difficulty; according to the invention, accurate knowledge feedback for individual differences is realized, and the pertinence and efficiency of industrial robot programming teaching are effectively improved.

The invention relates to the technical field of cigarette production, in particular to an automatic auxiliary material tray matching work station suitable for full-specification production. Comprising an industrial robot module, a 3D visual camera module, a code reader module, a tool clamp module, a clamp storage module, a cigarette auxiliary material and a tray. The tool clamp module and the clamp storage module are arranged on the two sides of the industrial robot module respectively, the 3D visual camera module is located above to-be-prepared auxiliary materials to be used for positioning and sending position information, the code reader module is arranged between an industrial robot and the prepared auxiliary materials to be used for scanning codes, and an automatic gun changing disc at the tail end of the industrial robot can switch corresponding clamps. And after the industrial robot receives the position information, the clamp is switched to grab the to-be-prepared auxiliary materials, and the auxiliary materials are placed on the prepared auxiliary material tray after being matched with the code reader to scan codes. Through multi-module cooperation, full-process automation of tray distribution is achieved, manual operation is reduced, production efficiency is improved, meanwhile, by means of accurate positioning and code scanning, the material recognition accuracy is guaranteed, and the workshop automation and intelligence level is remarkably improved.

The invention discloses an intelligent manufacturing feeding method and system fusing three-dimensional vision and robot cooperation, and belongs to the technical field of robot automation control. Three-dimensional point cloud data in a feeding area is acquired, and a workpiece space topological relation graph is constructed through adaptive clustering and boundary extraction; generating a task association tensor in combination with the physical attribute of the workpiece and a scheduling rule; the method comprises the following steps: collecting operation state information of a plurality of industrial robots, and constructing an action capability tensor; based on the task association tensor and the action capability tensor, an optimal task allocation matrix is generated through a multi-target collaborative optimization algorithm, and dynamic matching between the robot and the workpiece is achieved; in combination with real-time point cloud feedback, the initial path is corrected and the tail end is adjusted, and the robot is driven to accurately execute a grabbing task; the intelligent feeding system has the advantages of being high in environment adaptability, high in dispatching intelligence degree, excellent in path control precision and the like, and is suitable for high-dynamic complex manufacturing scenes.

The invention relates to the technical field of industrial automation, in particular to an industrial robot posture recognition method. Aiming at the problems of weak workpiece texture, strong surface reflection, serious stacking and shielding and the like in an industrial production line, a pixel-level dense feature fusion and self-attention mechanism is introduced, and semantic texture information of a color image and spatial geometric information of a depth image are integrated in a feature extraction stage; the problem that the posture of a rotationally symmetrical workpiece is fuzzy is solved through an asymmetric loss function, meanwhile, a comprehensive grabbing scoring model containing force sealing stability, environment collision risk probability, mechanical arm kinematics reachability and visual uncertainty is further established, and through the mode of combining off-line grabbing candidate construction and on-line real-time evaluation, the grabbing accuracy of the mechanical arm is improved. And the globally optimal grabbing pose is screened out. According to the method, the recognition precision and robustness of the industrial robot in the unstructured environment are remarkably improved, and the success rate and safety of grabbing operation are improved.

The invention relates to an industrial robot fault diagnosis method, device and equipment and a computer readable medium. The method comprises the steps that multiple pieces of operation state data of the industrial robot are acquired; preprocessing the operation state data to obtain enhanced data; performing feature extraction on the enhanced data, and constructing an operation feature vector of the industrial robot based on the extracted features; calculating the evidence support degree of each feature subset for different faults in the operation feature vector; and fusing all the evidence support degrees to obtain a fusion support degree, and determining that the industrial robot has a fault under the condition that the fusion support degree is greater than a preset alarm threshold value. According to the method, the multi-source operation state data is collected, preprocessing, feature vector construction and multi-feature subset evidence support degree calculation and fusion are performed, the multi-dimensional operation state of the robot is covered, the risk of misjudgment of a single feature is reduced, accurate fault recognition is achieved, and the technical problem that the accuracy of robot fault prediction is low is solved.

The invention discloses a dynamic visual guidance method and system for cooperative assembly of multiple mechanical arms, and belongs to the technical field of industrial robot visual guidance, and the method comprises the steps: synchronously collecting an image sequence through a binocular visual system, synchronously recording the pose sequence of each mechanical arm through a robot controller, and obtaining an observation data set; obtaining a two-dimensional trajectory flow of vehicle body assembly area feature points and instrument body feature points through an optical flow tracking algorithm, and obtaining a hand-eye matrix correction through a nonlinear optimization algorithm; obtaining an updated hand-eye matrix and a local pixel compensation field through a spatial interpolation algorithm based on the re-projection error of each feature point; according to the method, self-adaptive compensation of flexible deformation of a vehicle body and dynamic reflection of an instrument is realized by adopting unsupervised clustering and spatial interpolation according to a re-projection error, and the precision of cooperative assembly of multiple mechanical arms and the self-adaptive capability of a system in a complex industrial environment are improved.